Longitudinal studies plan an increasingly prominent role in biomedical research. Examples from the cancer field include, analysis of repeated tumor marker measurements, tumor growth curve analysis for the animal bioassay, and changes in body weight for patients on cancer clinical trails. In the past, analysis of such data were based on strong, but unverifiable, parametric assumptions about the data generating process. Recently there have been several important methodological advances that enable longitudinal data to be analyzed by nonparametric and semi- nonparametric methods. Moreover, these methods remain valid in the presence of missing data and time varying covariates. The main goal of this phase I SBIR proposal is: (a) to unify these methodologic advances by providing a single semi-nonparametric (quasi-likelihood) approach that can be used with either continuous or categorical repeated measures data, with possibly missing observations, and possibly time varying covariates; (b) to demonstrate the feasibility of developing statistical software meeting commercial standards of friendlines and reliability, to implement the new analytical techniques; (c) to analyze some real longitudinal data sets with the newly developed demonstration software. The experience gained in this phase I endeavor will be used during phase II to develop a comprehensive, commercially viable statistical package for longitudinal data analysis on mainframe, mini, and micro-computers.